Existing point cloud denoising methods can be divided into traditional geometric approaches [1]}, [2]}, [3]}, [4]}, [5]}, [6]}, [7]} and deep learning-based approaches [8]}, [9]}, [10]}, [11]}, [12]}, [13]}, [14]}. Among them, BF is one of the most widely-used denoising methods. It was first presented by Tomasi and Manduchi [15]} for image denoising and well adapted to 3D mesh denoising by Fleishman et al. [16]}. Following the experience of BF in mesh denoising, Digne et al. [1]} extend it to point cloud denoising for the first time. Although BF has achieved satisfactory denoising performance, it still has some defects.
When dealing with point clouds of different point densities and noise intensities, BF requires continual manual tuning of parameters, but finding the best parameters is non-trivial.
Besides, BF and its variants take the same parameters for all points in the whole point cloud, which leads to under- or over-smoothing of data with rich geometric structures (corners, smooth features, and sharp edges) and multiple noise intensities.
Thus, it is inappropriate to process all points of a noisy point cloud with the same parameters.
